用机器学习检测灯塔传感器定时故障，精度可达10-15分钟。
Using machine learning for fault detection in lighthouse light sensors
- 基于多层感知机的时序异常检测方法
- 可识别10-15分钟级别的运行时间偏差
- 适合海上安全监控与自动化运维场景
灯塔通过光敏电阻传感器根据昼夜变化启停灯光，以保障航海安全。然而传感器故障可能导致灯光开启或关闭时间逐渐偏移，影响航行安全。本文提出一种基于机器学习的自动故障检测方法，评估了决策树、随机森林、极端梯度提升和多层感知机四种算法。实验结果表明，多层感知机表现最佳，能够检测出10-15分钟级的时间偏差，具备高精度与实用性，是实现灯塔传感器自动化故障诊断的有效工具。
原文摘要 · Abstract (English)
Lighthouses play a crucial role in ensuring maritime safety by signaling hazardous areas such as dangerous coastlines, shoals, reefs, and rocks, along with aiding harbor entries and aerial navigation. This is achieved through the use of photoresistor sensors that activate or deactivate based on the time of day. However, a significant issue is the potential malfunction of these sensors, leading to the gradual misalignment of the light's operational timing. This paper introduces an innovative machine learning-based approach for automatically detecting such malfunctions. We evaluate four distinct algorithms: decision trees, random forest, extreme gradient boosting, and multi-layer perceptron. Our findings indicate that the multi-layer perceptron is the most effective, capable of detecting timing discrepancies as small as 10-15 minutes. This accuracy makes it a highly efficient tool for automating the detection of faults in lighthouse light sensors.
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